Multiple kernel local Fisher discriminant analysis for face recognition

Dimensionality reduction algorithms, which aim to obtain low-dimensional feature representation with enhanced discrimination power, have attracted great attention for face recognition. Local Fisher discriminant analysis (LFDA) is a recently developed linear dimensionality reduction algorithm. It has been shown that LFDA is a strong analyzer of high-dimensional data. However, LFDA is a linear method, and this makes it difficult to describe the complex nonlinearity of face images. In addition, LFDA only focuses on using a single data descriptor to depict the whole face image dataset, while lacks a systematic way of integrating multiple image features for dimensionality reduction. To enhance the performance of LFDA in face recognition, a new algorithm termed multiple kernel local Fisher discriminant analysis (MKLFDA) is proposed in this paper. MKLFDA produces nonlinear discriminant features via kernel theory, and considers multiple image features with multiple base kernels. Experimental results on three face databases demonstrate the effectiveness of the proposed algorithm.

► We propose the MKLFDA algorithm for dimensionality reduction. ►It gets maximum discrimination via intraclass geometry and interclass separability. ► It can effectively use multiple image features captured in different descriptors. ► It produces nonlinear discriminant features by using multiple kernel functions.